A consultant I worked with once had a strict rule: never walk into a client meeting with a number you can’t trace back to source.

She didn’t care how good it looked on a slide or how confidently someone said it. If she couldn’t defend it in front of a global access lead, it didn’t go in the deck.

That mindset hasn’t changed. If anything, it matters more now.

Because as AI tools start generating more content, more summaries, more “insights,” the question every consultant should be asking is simple:

Where did this come from?

If you can’t answer that, you’re not just risking rework.

You’re risking trust.

The black box problem

Most AI feels like magic until it goes wrong.

It gives you a paragraph, an answer, a draft GVD section… and it looks great.

But then someone asks a perfectly fair question.

“What’s this based on?”

And suddenly you’re scrolling through PubMed or trying to recreate the prompt trail.

This is the black box problem.

AI that outputs things you can’t trace, check, or justify.

In market access, that’s not just inconvenient.

It’s unusable.

What is explainable AI?

Explainable AI is exactly what it sounds like.

It tells you what it did, why it did it, and where it got the information from.

It’s not just about accuracy.

It’s about auditability.

It’s about being able to walk a client or regulator through your logic and say, “This came from here. This is why we included it. And this is how it connects to the strategy.”

In market access, that level of transparency is non-negotiable.

You don’t just need outputs.

You need outputs that can be defended.

Why it matters more in consulting

Consultants aren’t just passively using AI.

They’re putting it in front of clients.

Basing recommendations on it.

Building arguments with it.

That changes the stakes.

Because if your AI gives you a payer insight, and that insight turns out to be fabricated, misattributed, or pulled from a low-quality source, your reputation takes the hit - not the model.

Explainable AI means:

• Every data point is source-linked
• Every summary is traceable
• Every output is built on something you can show, check, and explain

It lets you move fast without losing rigour.

It protects your brand as much as your work.

How to get it right

Not all explainable AI is equal.

And slapping a footnote on a generated paragraph doesn’t count.

Here’s what good looks like in market access:

1. Verified sources only
2. The system should only pull from trusted, relevant datasets - like clinical trials, HTA reports, validated internal assets - not whatever it scrapes from the web.
3. Inline traceability
4. Every claim, insight, or value driver should link directly to its origin. So when a client asks, “Where’s this from?” you can click and show them.
5. Transparent ranking logic
6. You should be able to see why one source or insight was surfaced over another. Not just that it exists, but that it was prioritised for a reason.
7. Structured workflows, not just summaries
8. Explainability isn’t just for outputs. It should also guide how data is pulled, organised, and transformed, so you can explain how the system thinks, not just what it says.

Why we built Knowledgeable this way

We didn’t add explainability as a feature.

We built the entire system around it.

Because we’ve been consultants.

We’ve been in the meetings where a slide gets challenged and the room turns cold.

We know that speed means nothing if you can’t trust what you’re delivering.

That’s why Knowledgeable delivers AI-powered insights that are always sourced, always traceable, and always defensible.

No black boxes.

No guesswork.

Just answers you can stand behind.

Because the future of consulting isn’t just faster.

It’s faster with credibility intact.